Empirical Case Research Articles

Innovation Integration and Empirical Research of Computer Technology in the Field of Applied Physics

With the rapid development of computer technology, the integration of computer science and applied physics has become an important force driving the progress of modern science and technology. In particular, the application of computer technology in areas such as experimental design, simulation and analysis, and data processing has significantly improved the efficiency and accuracy of research. This paper explores the direction of integration between computers and applied physics, focusing on innovative applications of computer technology in fields like quantum physics and materials science. Through specific empirical research cases, it demonstrates the driving role of computer technology in advancing applied physics research and engineering applications. By combining theory and practice, this paper aims to provide insights for further integration of computer science and applied physics, and to look ahead to future research prospects.

Efficient Implementation of AI Agents in Enterprise Application Integration (EAI) and Electronic Data Interchange (EDI)

Contemporary enterprises increasingly contend with the multifaceted challenge of integrating diverse applications and managing large-scale data flows. Although foundational, traditional Enterprise Application Integration (EAI) and Electronic Data Interchange (EDI) solutions often fail to address the real-time processing, intelligent automation, and dynamic adaptation demanded by today’s digital landscape. The study investigates the incorporation of Artificial Intelligence agents into EAI and EDI ecosystems to surmount these limitations. We posit that the autonomous, reactive, proactive, and social attributes intrinsic to AI agents render them particularly adept at advancing integration capabilities. This research introduces an innovative agent-based architectural framework focusing on sophisticated routing, dynamic data transformation, and automated workflow orchestration. The proposed system leverages machine learning algorithms for adaptive data mapping and employs reinforcement learning to optimize agent collaboration and resource allocation in the existing EAI/EDI infrastructure. Empirical evaluations, comprising simulations and case analyses, reveal pronounced improvements in key performance metrics such as transaction throughput, latency reduction, and error minimization relative to traditional integration methodologies. This whitepaper expounds on the strategies and methodologies integral to the effective deployment and operationalization of Agentic AI within EAI and EDI contexts. It delineates the architectural refinements crucial for AI-driven transformation and proposes deployment frameworks designed to maximize scalability and resilience through AI orchestrators. By addressing the inherent complexities and offering tailored solutions, this study provides actionable insights for enterprises seeking to harness autonomous AI to bolster efficiency, curb operational costs, and unlock new pathways for innovation.

Unity of opposites: a structured way to promote geographic understanding

ABSTRACT Unity of opposites, as a key manifestation of the theory of contradiction, has embedded in ancient naive world view of the spatial dialectics raised by philosophers as well as Marxist geographers. Geography, as a discipline with a long history, emphasizes the implementation of description and illustration. However, as the phenomena to be explained become more complex, sophisticated methodologies such as spatial analysis and statistical models have been extensively used to explore the deeper mechanisms. Such a progress shows that geographic understanding has gradually moved from the empirical domain to the real domain, in which critical realism has been regarded as a pivotal way to reveal the ontology of geography. Within the scope of critical realism, in this paper, we propose that unity of opposites provides a structured way to distinguish geographical mechanisms as well as to underpin the design of empirical studies. We use two empirical cases to show that unity of opposites as a structured way of thinking corrects the biased spatial description, contributing to the formation of an inclusive and conclusive empirical framework. Last but not least, we call for further integration of the idea of unity of opposites in the future geographic research. In doing so, the current ambiguities in pluralistic mechanisms can be better addressed.

Spatial and temporal contexts of formal social control and system involvement: U.S. Latinos under immigration policing

AbstractSystem avoidance refers to the tendency of individuals who are concerned about formal social control (e.g., incarceration, immigration enforcement, or the removal of children from their families) to avoid surveilling institutions that engage in recordkeeping. While this research locates concerns about formal social control in an individual’s sanctionable status, the laws, policies, and practices that generate the threat of formal social control vary across space and time. Drawing on theories of legal consciousness, this article posits that spatial and temporal variation in the threat of formal social control has differential associations with whether and to what degree individuals with a sanctionable status report involvement in surveilling institutions. Our empirical case is U.S. immigration policing, which burdens Latinos across citizenship statuses. We link individual-level data on institutional involvement from the American Time Use Survey with administrative data on immigration policing across state-years. Results from double-hurdle models show that Latinos in state-years with higher rates of immigration policing (1) are less likely to report involvement in surveilling institutions but, (2) conditional on any involvement, do not vary in the time reported involved. We evaluate variations by nativity, citizenship status, institution, and the presence of sanctuary policies that circumscribe immigration policing. We conclude that the threat of formal social control across space and time implicates the situational meanings of institutional involvement for subordinated populations.

Scaling Nature‐Based Solutions for Fluvial Floods: A Worldwide Systematic Review

ABSTRACTDespite increased understanding and adoption of nature‐based solutions (NBSs) within urban and coastal areas, large‐scale NBS for fluvial flood mitigation remain challenging to study and implement. A stronger evidence base is needed to identify critical research gaps and to best inform the design and deployment of NBS on the watershed scale. We synthesize evidence of the performance and co‐benefits of NBS for fluvial flood mitigation based on a systematic review of 131 peer‐reviewed papers worldwide, developing an Ecosystem Focus Type (EFT) to compare flood mitigation across large‐scale NBS. While we find that NBS can mitigate fluvial floods across all EFTs, our study also highlights that inconsistencies in measurement methods, a dearth of empirical case studies, and large variability in reported values limit generalization and comparison across NBS. Co‐benefits for fluvial flood NBS are numerous, but few are quantified, and study methods vary with regard to specific NBS. Social benefits of NBS, including benefits to communities most in need of support, are infrequently part of these studies. There is a clear need to develop common design and performance standards for large‐scale NBS and for guidance on which measures are key to consider and monitor for flood mitigation and co‐benefits. The success of large‐scale NBS for fluvial flood mitigation will depend on research and practice guided by transdisciplinary systems thinking approaches that can deliver evidence‐based, community‐driven outcomes.

Production of medical knowledge between resilience and sharing: the case of a community of practice at the time of COVID-19

ABSTRACT This paper is focused on the production and circulation of medical knowledge ‘from below’ in the early stages of the COVID-19 pandemic. Specifically, the aim is to investigate the link between personal and professional resilience and the sharing of knowledge and practices at a time of epistemic rupture. The empirical case intends to discuss a Community of Practice (CoP) formed by a network of health professionals spontaneously activated in northern Italy during the first pandemic waves. Dynamics within the network were examined through a grounded theory approach, bringing out some analytical categories from the empirical field – 18 video interviews. Emerging narratives within the CoP reaffirm the value of medical practice, highlighting how in a condition of structural crisis expertise can give meaning to individual action through shared processes of signification, filling the temporary absence of recognized and established knowledge: recognizing the relevance of CoPs in knowledge production and circulation could better integrate them into the healthcare organization to promote systemic change and sustainability.

A SYSTEMATIC REVIEW OF DEMAND FORECASTING MODELS FOR RETAIL E-COMMERCE ENHANCING ACCURACY IN INVENTORY AND DELIVERY PLANNING

In the rapidly evolving landscape of retail e-commerce, characterized by volatile consumer behavior, diverse product assortments, and fluctuating market conditions, demand forecasting has emerged as a critical strategic capability. Accurate demand forecasting not only underpins effective inventory management and delivery planning but also serves as a cornerstone for optimizing supply chain responsiveness, minimizing stockouts and overstock situations, and enhancing customer satisfaction. As e-commerce platforms increasingly embrace data-driven operations, the role of predictive analytics in shaping demand planning strategies has gained unprecedented prominence. This systematic review presents a comprehensive synthesis of 72 peer-reviewed academic articles, industry reports, and empirical case studies published between 2010 and 2024. The objective is to evaluate the breadth, depth, and evolution of demand forecasting methodologies specifically applied within the context of retail e-commerce. The selected studies were rigorously screened and categorized based on methodological foundations, technological sophistication, and practical applications. Four major categories of forecasting models are examined: (1) traditional statistical approaches, such as AutoRegressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and exponential smoothing models; (2) machine learning techniques, including decision trees, random forests, support vector regression, and ensemble methods; (3) hybrid frameworks that integrate statistical modeling with machine learning or deep learning components; and (4) advanced deep learning architectures, particularly Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Convolutional Neural Networks (CNNs), and transformer-based models. The findings reveal distinct performance advantages across model categories. Traditional statistical models demonstrate continued relevance in scenarios marked by stable demand patterns, short forecasting horizons, and limited data complexity. However, their limitations become evident when applied to highly nonlinear, sparse, or volatile datasets. In contrast, machine learning models offer enhanced adaptability and accuracy, especially in handling high-dimensional data environments with diverse product lines and unpredictable promotional impacts. Deep learning models further advance this capability by capturing complex temporal dynamics, long-range dependencies, and multivariate input streams. These models are particularly effective for SKU-level forecasting, where product-specific demand patterns fluctuate frequently and require real-time recalibration. Many reviewed studies adopted modular architectures that allow domain-specific tuning and facilitate deployment within enterprise resource planning (ERP) and inventory management systems.

Disentangling nonrandom structure from random placement when estimating β‐diversity through space or time

AbstractThere is considerable interest in understanding patterns of β‐diversity that measure the amount of change in species composition through space or time. Most hypotheses for β‐diversity evoke nonrandom processes that generate spatial and temporal within‐species aggregation; however, β‐diversity can also be driven by random sampling processes. Here, we describe a framework based on rarefaction curves that quantifies the nonrandom contribution of species compositional differences across samples to β‐diversity. We isolate the effect of within‐species spatial or temporal aggregation on beta‐diversity using a coverage standardized metric of β‐diversity (βC). We demonstrate the utility of our framework using simulations and an empirical case study examining variation in avian species composition through space and time in engineered versus natural riparian areas. The primary strengths of our approach are that it provides an intuitive visual null model for expected patterns of biodiversity under random sampling that allows integrating analyses across α‐, γ‐, and β‐scales. Importantly, the method can accommodate comparisons between communities with different species pool sizes, and it can be used to examine species turnover both within and between meta‐communities.

The impact of digitalization on the development of jewelry retailing

This article is devoted to the study of the impact of digitalization on the development of jewelry retailing. The paper provides a comprehensive analysis including historical and theoretical foundations of digital transformation of the industry, empirical case study of Pozolotka company and assessment of the role of digital technologies in the formation of sustainable competitive advantage in jewelry retailing. The study demonstrates how the gradual transition from traditional stores to network trade and online platforms is accompanied by the introduction of modern Big Data tools, cloud technologies, automation and digital marketing. The empirical analysis on the example of the company “Pozolotka” shows that consistent application of digital solutions (online platforms, quick feedback systems, voting for new designs) contributes to expanding the customer base, improving the level of service and entering the international market. The article develops the concept of “superdrivers” of digitalization, which integrates various technological directions and serves as a basis for the formation of competitive advantages. The methodological basis of the research includes system analysis, comparative analysis and causal modeling. The practical significance of the work lies in the possibility of applying the results obtained to develop effective strategies of digital transformation in jewelry retailing.

Chuckles of Authority: Navigating Political Humor in China Through the Political Communication Process. The Li Haoshi Incident and Trump–Clinton Duet

Political humor is rarely found in the public political discourse or used to depict the political circumstances in China. This does not mean, however, that political humor is absent or irrelevant. From a political sociology perspective, Ding (2017) argues that it is an opportune moment for high-quality political humor to emerge in China; meanwhile, there is a growing perception that China is becoming increasingly authoritarian, particularly under the leadership of President Xi. Therefore, this paper will explore the relationship between political humor and political authority in China, by asking: How do political authorities in China respond to humor, and in what ways do they utilize or instrumentalize political humor, and for what purposes? The existing literature on political humor in China tends to overlook the multifaceted functions it serves and the diverse responses of citizens to political authority. By adopting a communicative, discursive rather than empowerment approach, the paper views political humor as a form of popular rhetoric and investigates its interaction with authority. It posits that political humor in China serves as both a political communication tool and a public discourse mode. The government strategically deploys it to shape public opinion in a manner that upholds the existing social order and legitimizes its governance and also fosters a nationalistic identity that aligns with its objectives. Relying on a qualitative content analysis, the article is structured as follows: first, the literature on political humor in China is reviewed; second, the concept of political legitimacy is discussed, and an analytical framework that employs a communicative and discursive approach are discussed; third, two empirical cases of political humor in China are explored (namely, the Li Haoshi incident in 2023, and the Trump–Clinton duet in 2016); and, finally, conclusions are offered to reflect the significance of political humor in Chinese politics.

Transforming Management Education: Integrating Sensorial, Emotional, and Spiritual Skills to Navigate Complexity

Higher education (HE) institutions and educators continually explore novel and practical pedagogical approaches to equip future managers with the skills to make effective decisions and navigate complex organizational environments. This paper presents an empirical case of a pioneering project that devised an innovative training methodology strategically using Sensorial, Emotional, and Spiritual (SES) skills, in addition to rational capacities, to address complex management cases. The methodology, rigorously tested over three years with students from four European universities, has demonstrated its potential to revolutionize management education and instilled confidence in its effectiveness. The results underscore the significant value of sensorial, emotional, and spiritual (SES) training within HE institutions. This training enhances the competencies necessary for future managers to tackle and resolve complex problems effectively, strengthening their ability to manage complex environments. The survey data and interviews we conducted indicate that sensory, emotional, and spiritual skills training has not only improved the competencies needed to manage complexity – awareness, sense of community, self and other regulation, and sense of life but has also transformed the students' approach to problem-solving. This transformative approach not only inspires hope for the future of management education but also suggests a potential for positive change. Our investigation presents Sensorial, Emotional, and Spiritual skills as a transformative subset of soft skills, advocating for the need for SES skills training for future managers. We discuss the implications for pedagogies and strongly advocate for including SES skills development in students’ training and learning at higher education institutions.

Implementation of People Analytics in HR Management to Improve Employee Performance and Job Satisfaction

The rapid development of technology and data analytics has transformed Human Resource (HR) management, particularly in improving employee performance and job satisfaction. This study explores the implementation of People Analytics in HR management using a qualitative approach through literature review and library research methods. People Analytics leverages data-driven insights to optimize recruitment, performance evaluation, employee engagement, and retention strategies. By systematically analyzing workforce data, organizations can make informed decisions that enhance productivity and job satisfaction. This study highlights the key benefits of People Analytics, including identifying performance gaps, predicting employee turnover, and fostering a more inclusive and engaging work environment. Findings from various academic sources suggest that organizations that integrate People Analytics experience improved decision-making processes and a more strategic HR function. However, challenges such as data privacy concerns, ethical considerations, and the need for skilled HR professionals to interpret data remain significant barriers to successful implementation. The study concludes that People Analytics can revolutionize HR management by promoting a data-driven culture that aligns employee needs with organizational goals. By adopting this approach, companies can create a more dynamic and responsive work environment that enhances employee satisfaction and overall business performance. Future research should focus on empirical case studies to further validate the impact of People Analytics in different organizational contexts.

Role of technology in outbound vehicle logistics: evidence from the Chinese automobile sector

PurposeThe COVID-19 outbreak and severe weather challenges have disrupted the supply chains of various industries across the globe, including the automotive industry. The purpose of this study is to investigate the outbound vehicle logistics practices of a Chinese automotive firm to achieve value chain optimization to meet the order fulfillment through the feasibility of using blockchain technology (BCT).Design/methodology/approachTo identify the research gap and to formulate the research questions, a detailed literature review was conducted. To compare and contrast the Chinese automotive outbound logistics process, we interviewed Original Equipment Manufacturers (OEMs), vehicle logistics service providers and local IT vendors. To study the feasibility of BCT in the case companies, we conducted interviews with the experts as part of the in-depth empirical case study. Case analysis has emerged as the research themes.FindingsIn-depth data analysis suggested three important areas that needed focus from OEMs (1) handling distribution constraints; (2) ability to monitor quality; and (3) good communication with network partners and receiving real-time alerts, especially during peak demand periods. Our findings confirm that OEMs can address all these challenges in the presence of BCT alliance frameworks with automotive supply chain partners.Research limitations/implicationsThe scope of the research is restricted to the Chinese automotive OEMs. This research can be extended to other countries practicing the same level of outbound vehicle logistics. Data collected in this research were generated from a data sheet which is subjective to some extent.Practical implicationsThis research provides practical insights for practitioners on the feasibility of using BCT on outbound logistics which can optimize order fulfillment and performance.Originality/valueThe paper adds insights into the feasibility of using BCT in automotive vehicle logistics to increase performance and compares the practices from China with developed economies.

Impact of Maize-legume Intercropping on Soil Fertility Management: A Review

This review paper aims to gather significant literature on the influence of maize-legume intercropping in soil fertility management based on research conducted in various parts of the world at various times. The review titled ‘Impact of maize-legume intercropping in soil fertility management was organized using relevant materials such as empirical studies, case reports and meta-analyses that highlight the benefits and challenges associated with this agricultural practice. By synthesizing these findings, the paper seeks to provide a comprehensive overview of how maize-legume intercropping can enhance soil health, improve crop yields and contribute to sustainable farming practices as journal article reviews and brief communications. Intercropping is a valuable agronomic approach that allows efficient use of resources such as water, soil nutrients and light, resulting in increased yield when compared to monocropping. This review provides researchers and practitioners with a thorough understanding of the benefits of intercropping in sustaining soil fertility, which greatly contributes to crop output. This review investigated and clarified the various ecological services provided by maize-legume intercropping systems. As different research studies conducted across the world illustrate, intercropping promotes nutrient availability and uptake by plants, thus increasing production. This knowledge will assist farmers in India in better understanding how to employ cereal-legume intercropping systems what benefits to raise yields, improve socioeconomic situations and promote conservation agriculture practices.

From embracement to attachment: Registering people on the move and the couplings of noncitizenship and territory

This article analyzes how people on the move encounter registration infrastructures on their journey to Europe. Through the lens of three empirical cases—registration in the Eurodac system, registration in the framework of the European temporary protection directive, and national registration of migrants on Hellenic islands—we explore what we term “noncitizenship-territory couplings.” The article develops the concept of “registration as attachment” to describe how sociotechnical procedures establish relationships between noncitizen subjects, mobility rights, and territorial space. We therefore ask how registration infrastructures couple territory and noncitizenship in Europe in different ways—that is, how such couplings shape the movement of people, enact territories, and endow spatial rights or restrictions. We argue that new and partial couplings of noncitizenship and territory are produced—the “containment coupling,” the “facilitation coupling,” and the “detainment coupling.” Each follows specific registration dynamics, enacts different territorial patterns, and simultaneously shapes migrant mobility and their rights. By situating the article at the intersection of science and technology studies, human geography, and citizenship studies, we wish to contribute to the growing literature on the performative effects of infrastructures in border and migration governance. At the same time, our intervention encourages further analysis of the varied spatialities of states and sovereign power that move beyond current discussions about denationalization and deterritorialization.

Beyond the Algorithm: The Promise and Paradox of AI in EFL Education

Artificial Intelligence (AI) has become increasingly integral to English as a Foreign Language (EFL) education, offering benefits such as scalability, personalized learning, and immediate feedback. Yet its capacity to engage with the broader dimensions of language learning—including creativity, cultural fluency, and aesthetic expression—remains contested. This review analyzes empirical case studies from AI-driven education platforms (e.g., GPT-4-assisted classes showing a 20% improvement in vocabulary retention) while placing these findings within an interdisciplinary framework that integrates aesthetic education and art studies. Despite AI’s proven efficiency in automating repetitive tasks, significant limitations persist in fostering intercultural communication, creativity, and emotional intelligence. By applying Gödel’s Incompleteness Theorem, this paper underscores the inherent constraints of formal systems and asks: To what extent can AI support the cognitive, emotional, and aesthetic dimensions of EFL learning? How can a hybrid model optimize these outcomes by combining AI with human creativity and cultural insight? Two main contributions distinguish this study: (1) an innovative hybrid learning model that explicitly merges AI-driven instruction with art-based EFL tasks (e.g., digital storytelling, intercultural narratives, and poetry composition), and (2) a broader theoretical lens that situates these practices within aesthetic education, thereby expanding on prior research that often overlooks the artistic and cultural facets of language acquisition. Ethical concerns such as algorithmic bias, data privacy, and dehumanization risk are also examined. The findings highlight practical recommendations for integrating AI into art-enhanced EFL curricula, emphasizing cultural nuance, creative exploration, and emotional engagement. Ultimately, this review demonstrates the potential synergy between technology and the human, artistic dimensions of language learning, offering insights for interdisciplinary fields spanning EFL, educational technology, and art studies.

Slippery substances: Accreting alternative chemical knowledges in a heavy industry port

How might chemicals slip unnoticed across boundaries and through bodies in a range of material and bureaucratic registers? This article responds by extending emergent theorisations of ‘slipperiness’ into the nascent field of chemical geographies. Slipperiness offers a framing for chemicals that challenges and also situates contemporary managerial regimes that subscribe to universalised concepts such as pollution thresholds. Breaking slipperiness into four key characteristics - stealth, dynamism, persistence, and slippages - this paper draws on an empirical case study of Port Kembla, a heavy industry brownfield port in Australia, to outline how industrial chemicals have been instrumental in the port's ongoing relations with the broader residential community. This research takes place at a pivotal juncture in history when such sites are being actively repurposed to support decarbonisation infrastructures. The article hones in on four interrelated themes that emerged from the fieldwork as avenues for exploring the characteristics and consequences of chemical slipperiness. The first theme is the role of historical chemical contamination in maintaining port boundaries. The second explores how governance of contaminants is grounded in the concept of thresholds. The third examines the role of new energy transition infrastructure in disturbing and redistributing historical chemical accretions. The fourth theme is the knowability and visibility of chemical contaminants for residential communities in spatial proximity with the port. This paper develops the concept of bureaucratic slippage and focuses on the potential of voluminous understandings of space to overcome or challenge governance regimes that otherwise would fail to identify and manage contaminant risks.

Injecting complexity in simulation models: Do selection and social influence jointly promote cooperation?

This paper employs a simulation model to investigate the effectiveness of cooperation selection (“selecting similar others”) and social influence (“do as others do”), since both mechanisms promote cooperation in theoretical analyses and experimental studies. However, it is unclear how effective cooperation selection and social influence are in simulation models where both mechanisms operate simultaneously alongside additional social dynamics, such as reciprocity and transitivity. This paper relies on a model loosely based on an empirical case in which students selected others based on how cooperative they perceive the other. Using existing theoretical cooperation models as a benchmark, we insert relational, behavioral, and contextual assumptions into our model and build on data from 95 students when we vary the strength of cooperation selection and social influence relative to empirically observed levels. We take co-evolution stochastic actor-oriented models as basis because the model inherently accounts for the interdependence of behavior and network selection. Our simulations reveal that cooperation benefits most when cooperation selection and social influence are strongly positive. Through the combination of cooperation selection and social influence, the simulations show that cooperators form dense local clusters, influencing their peers to keep cooperating while insulating themselves from social influence from defectors. Robustness checks confirm the stability of these findings across diverse parameter configurations.

A novel damping discrete grey model with time-polynomial term for forecasting monthly natural gas consumption

PurposeThis study aims to design a novel seasonal discrete grey model for forecasting monthly natural gas consumption by incorporating damping accumulation and time-polynomial term.Design/methodology/approachConsidering the principle of new information priority and nonlinear patterns in the original series of monthly natural gas consumption, we establish a novel discrete seasonal grey model by adding the damping accumulation and time-polynomial term into the existing model. In addition, the order of damping accumulation and the coefficient of time-power term can be determined by the moth flame optimization (MFO) algorithm.FindingsThe empirical cases show that the proposed model has a better prediction performance when compared with other benchmark models, including six seasonal grey models, one statistical model and one artificial intelligent model. Based on forecasts, the proposed model can be considered a promising tool for monthly natural gas consumption (NGC) in US.Originality/valueBy combining the damping accumulation and the time-polynomial term, a new discrete seasonal grey model for improving the prediction performance of the existing grey model is proposed. The properties of the proposed model are given, and the newly-designed model is initially applied to predict monthly NGC in US.

Reimagining Art and Design Education: An AI-Enhanced Interdisciplinary Project-Based Pedagogical Framework

This study investigates the paradigm shift in interdisciplinary art and design education driven by artificial intelligence (AI) advancements, proposing an "AI-Interdisciplinary-Project" pedagogical model validated through the empirical case of Guyi Garden cultural product design. By integrating generative AI technologies (e.g., Stable Diffusion), a STEAM-E (Science, Technology, Engineering, Arts, Mathematics, and Ethics) knowledge framework, and agile project-based learning, we establish a dynamic pedagogical cycle comprising three innovation layers: AI-accelerated cultural symbol extraction (40% efficiency gain), human-AI co-creation workflows (reduced design iteration cycle to 5.3 days/prototype), and ethically constrained social validation (82.4 user satisfaction score). Empirical results demonstrate significant educational outcomes, with a 27% enhancement in students' interdisciplinary collaboration competence and the development of 8 commercially viable product prototypes, while effectively bridging traditional architectural motifs with contemporary design paradigms. The research further articulates a "techno-humanistic equilibrium" framework supported by an open-source toolchain ecosystem, providing replicable strategies for AI-era design education innovation. Its applicability extends to rural intangible cultural heritage revitalization and inclusive product development, catalyzing synergistic evolution among educational, industrial, and cultural ecosystems through techno-cultural hybridization.